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Recent summer precipitation trends in the Greater Horn of Africaand the emerging role of Indian Ocean sea surface temperature
A. Park Williams • Chris Funk • Joel Michaelsen •
Sara A. Rauscher • Iain Robertson • Tommy H. G. Wils •
Marcin Koprowski • Zewdu Eshetu • Neil J. Loader
Received: 5 August 2011 / Accepted: 10 October 2011
� The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract We utilize a variety of climate datasets to
examine impacts of two mechanisms on precipitation in the
Greater Horn of Africa (GHA) during northern-hemisphere
summer. First, surface-pressure gradients draw moist air
toward the GHA from the tropical Atlantic Ocean and
Congo Basin. Variability of the strength of these gradients
strongly influences GHA precipitation totals and accounts
for important phenomena such as the 1960s–1980s rainfall
decline and devastating 1984 drought. Following the
1980s, precipitation variability became increasingly influ-
enced by the southern tropical Indian Ocean (STIO) region.
Within this region, increases in sea-surface temperature,
evaporation, and precipitation are linked with increased
exports of dry mid-tropospheric air from the STIO region
toward the GHA. Convergence of dry air above the GHA
reduces local convection and precipitation. It also produces
a clockwise circulation response near the ground that
reduces moisture transports from the Congo Basin.
Because precipitation originating in the Congo Basin has a
unique isotopic signature, records of moisture transports
from the Congo Basin may be preserved in the isotopic
composition of annual tree rings in the Ethiopian High-
lands. A negative trend in tree-ring oxygen-18 during the
past half century suggests a decline in the proportion of
precipitation originating from the Congo Basin. This trend
may not be part of a natural cycle that will soon rebound
because climate models characterize Indian Ocean warm-
ing as a principal signature of greenhouse-gas induced
climate change. We therefore expect surface warming in
the STIO region to continue to negatively impact GHA
precipitation during northern-hemisphere summer.
Keywords African monsoon � Indian Ocean warming �Drought � Moisture transports � Energy flux � Tree rings �Stable isotopes � Global warming � Food security
1 Introduction
At the eastern boundary of the Sahel lies the Greater Horn
of Africa (GHA), a region of northeastern Africa with
highly food-insecure countries that are particularly
A. P. Williams (&)
Earth and Environmental Sciences Division,
Los Alamos National Laboratory, Los Alamos, NM, USA
e-mail: parkwilliams@lanl.gov
A. P. Williams � C. Funk � J. Michaelsen
Geography Department, University of California,
Santa Barbara, CA, USA
C. Funk
Earth Resources Observation and Science (EROS),
U.S. Geological Survey, Sioux Falls, SD, USA
S. A. Rauscher
Theoretical Division,
Los Alamos National Laboratory, Los Alamos, NM, USA
I. Robertson � N. J. Loader
Department of Geography, College of Science,
Swansea University, Swansea, UK
T. H. G. Wils
Department of Geography, Rotterdam University,
Rotterdam, The Netherlands
M. Koprowski
Laboratory of Dendrochronology, Institute of Ecology
and Environment Protection, Nicolaus Copernicus University,
Torun, Poland
Z. Eshetu
Forestry Research Centre, Ethiopian Institute of Agricultural
Research, Addis Ababa, Ethiopia
123
Clim Dyn
DOI 10.1007/s00382-011-1222-y
vulnerable to interannual and decadal swings in precipita-
tion totals (Funk et al. 2008; Funk and Brown 2009). Both
the GHA and Sahel receive the bulk of their precipitation
during the June–September (JJAS) boreal summer season.
These regions experienced a significant decline in JJAS
precipitation during the 1970s and 1980s due to anomalous
warming of the South Atlantic and Indian Oceans (Folland
et al. 1986; Hastenrath 1990; Giannini et al. 2003; Hoerling
et al. 2006; Cook 2008) and subsequent shifts in moisture
transports and upper-air flow (Nicholson 2000, 2009b;
Nicholson and Grist 2003). Following the 1980s, warming
in the North Atlantic appears to have caused a precipitation
recovery throughout much of the Sahel (Giannini et al.
2008; Hagos and Cook 2008). In the GHA, however, the
extent of post-drought recovery is unclear. Recent studies
suggest drying over GHA may have continued into the
2000s (Seleshi and Zanke 2004; Funk et al. 2011b), but the
cause of this potential drying and decoupling of precipi-
tation trends in the GHA from those of the Sahel remains
unexplored.
Previous studies have identified a variety of drivers of
JJAS precipitation and drought in the GHA. While JJAS
wind trajectories toward the GHA are quite variable from
year to year, the rainforest region of the Congo Basin
appears to be the main moisture source to Sudan and much
of the Ethiopian Highlands, and the Indian Ocean is the
main moisture source for eastern portions of Ethiopia and
Kenya (Camberlin 1997; Gatebe et al. 1999; Riddle and
Cook 2008; Levin et al. 2009). South of the center of the
North African surface low, deep convection and enhanced
moisture transport from the south and west fuel monsoonal
precipitation during JJAS in the Sahel and GHA. Histori-
cally, for both the Sahel and GHA, the primary driver of
interannual and longer-term variability of summer precipi-
tation is the north-south displacement of jets, zone of
maximum convection, and southern boundary of the ther-
mal low over northern Africa (Nicholson 2009b), all of
which appear to be influenced by tropical sea-surface
temperature (SST) anomalies (Bhatt 1989; Whetton and
Rutherfurd 1994; Camberlin 1995, 1997; Riddle and Cook
2008; Kucharski et al. 2009; Segele et al. 2009a). Some
research also suggests that increased tropospheric aerosol
concentration works to suppress the northward migration of
the JJAS rainbelt into northern Africa, potentially reducing
precipitation in the GHA (Rotstayn and Lohmann 2002).
Further, relationships exist between the strength of the
Indian monsoon and GHA precipitation (Camberlin 1997).
Overall, drought in the GHA tends to occur when there is a
reduction in the amount of moisture reaching the GHA
from the tropical Atlantic Ocean and Congo Basin region.
Recently, a number of studies suggested that increased
SSTs in the STIO may cause large-scale changes in
atmospheric circulation and divert important moisture
transports away from the GHA, altering previous rela-
tionships between regional circulation and precipitation
(Verdin et al. 2005; Funk et al. 2008; Hagos and Cook
2008; Williams and Funk 2011). In an analysis of boreal
spring (March–June, MAMJ), Williams and Funk (2011)
found that higher SSTs in the STIO have driven large
increases in convection and precipitation over the STIO.
The resulting increase in the amount of diabatic energy
released during precipitation has led to increased diver-
gence of dry static energy (DSE) in the mid- and upper-
troposphere. During MAMJ, this intensified outflow of
mid- and upper-tropospheric DSE from the STIO has
increased subsidence over northern Africa and decreased
moisture transport into the GHA for at least the past
30 years. The decrease in the amount of moisture trans-
ported into the GHA region appears to have caused a
reduction in what MAMJ precipitation totals would have
otherwise been in the absence of increased SSTs in the
STIO. Therefore, increased SSTs in the STIO and
subsequent changes in moisture transports may signify a
long-term alteration in how interannual variability in
atmospheric circulation impacts interannual variability in
precipitation totals throughout much of the GHA.
While no existing data sources detail the true sources of
GHA precipitation, the composition of stable isotopes in
Ethiopian rainwater suggests that most of the moisture is
derived from the rainforests of the Congo Basin (Sonntag
et al. 1979; Rozanski et al. 1996; Levin et al. 2009). Rain-
water molecules in the Ethiopian Highlands and sur-
rounding areas have exceptionally high levels of the heavy
stable isotope of oxygen (18O) (Dansgaard 1964; Levin
et al. 2009). The high level of 18O relative to 16O (hereafter
referred to using the standard d18O terminology) is evi-
dence that the Congo Basin is a primary source of moisture
to the GHA because moisture transpired by the wet forests
of the Congo Basin does not undergo isotopic fractionation
(Zimmermann et al. 1967; Salati et al. 1979; Gat and
Matsui 1991). Unfortunately, isotopic records of precipi-
tation throughout the GHA are inadequate to determine
whether there is a trend in the proportion of precipitation
coming from the humid continental interior. Conveniently,
trees use rainwater to create cellulose during photosyn-
thesis, and d18O in precipitation is partially conserved in
cellulose (Yapp and Epstein 1982; Roden and Ehleringer
1999a; Roden et al. 2000; McCarroll and Loader 2004;
Gessler et al. 2009). In trees that produce annual growth
rings, the isotopic composition of cellulose in tree rings
may reflect an annual record of d18O in precipitation
(Roden and Ehleringer 1999a; Robertson et al. 2001;
Anchukaitis and Evans 2010). We will investigate whether
this is the case for trees in the Ethiopian Highlands.
In this study we use a unique network of over 1,200
publicly and privately available gauge records from
A. P. Williams et al.: Recent summer precipitation trends
123
northern and eastern Africa to calculate a best-estimate
record of JJAS precipitation for the GHA through 2009.
Where sufficient precipitation data are available, we
quantify the degree to which the GHA has recovered rel-
ative to the 1970–1989 mean. We then identify the atmo-
spheric mechanisms that governed interannual variability
in GHA precipitation during the period of 1948–1989 and
determine whether the relationships between GHA pre-
cipitation and these mechanisms remained stable from
1990 to 2009. We utilize reanalysis climate data and a tree-
ring isotope record from the Ethiopian Highlands to test the
possibility that rapidly rising SSTs in the Indian Ocean
have acted to suppress JJAS precipitation in the GHA
following the peak of the Sahel drought in the 1980s.
2 Data and methods
2.1 Precipitation data
As the primary basis for evaluating precipitation trends
throughout the Sahel and GHA, we use 0.25� spatial inter-
polation of a dense set of observational gauge precipitation
records (Funk et al. 2003, 2007, 2011b; Funk and
Michaelsen 2004; Funk and Verdin 2009). This dataset
(CHG-CLIM) was developed by the Climate Hazards
Group (CHG) at the University of California, Santa
Barbara. The gridded product was constructed using qual-
ity-controlled rain-gauge data from 109 stations in Sudan,
210 stations in Ethiopia, 57 stations in Uganda, 144 stations
in Kenya, and 817 stations throughout other Sahel and east
African countries. Because the CHG is an active member of
the Famine Early Warning Systems Network (FEWS NET)
and routinely obtains and analyzes up-to-date meteorolog-
ical records to support decision-making processes for food
security, the CHG archive has substantially more recent
observations than those found in standard global station
archives. Figure 1 shows maps of mean gridded JJAS pre-
cipitation totals, gridded mean standard-error estimates,
station locations, each station’s percent of reported monthly
JJAS data from 1948 to 2009, and a plot of the number of
reporting stations in the Greater Horn region during each
JJAS season. The CHG-CLIM dataset and underlying
methodologies are described in detail in Funk et al. (2011b)
and summarized in the ‘‘Appendix’’.
2.2 Seasonal precipitation record for the Greater
Horn of Africa
We limit our region of focus to areas within Sudan, Ethi-
opia, Uganda, and Kenya (these countries are outlined in
green in Fig. 1) where the following criteria are met: (1)
mean JJAS precipitation during 1990–2009 is less than that
of 1970–1989, (2) mean JJAS precipitation exceeds 75% of
mean MAMJ precipitation, which is the dominant rainy
season immediately south of our region of interest, and
(3) mean JJAS precipitation exceeds 150 mm. The study
area includes southern Sudan, western Ethiopia, northern
Uganda, and parts of western Kenya. While the term
‘‘GHA’’ is generally used to refer to a quite larger region,
we use the term to refer only to our study area defined here.
Our GHA region is outlined in red in all map figures
beginning with Fig. 2.
To calculate a time series of JJAS precipitation for the
GHA, we first standardized (converted to z-scores) each
grid cell’s time series by subtracting its mean and dividing
by its standard deviation. We then calculated the region’s
average standardized anomaly from each JJAS season
during 1948–2009. The resulting time series of mean sea-
sonal anomalies for the region accounts for 59% of the
interseasonal variability throughout the region. This mean
Fig. 1 CHG-CLIM precipitation during JJAS. a Mean total JJAS
precipitation during 1948–2009. b Standard error reported as percent
of mean. c Station locations and % of JJAS months from 1948 to 2009
with non-missing data. d Time series of the average number of
stations reporting a monthly precipitation total for each of the JJAS
months. Dotted lines bound the inner quartiles. The black linerepresents the number of stations within the black box in c. The greenline represents the number of stations in the green polygon in c, which
bounds Sudan, Ethiopia, Uganda, and Kenya
A. P. Williams et al.: Recent summer precipitation trends
123
precipitation time series is most representative of precipi-
tation in the central portion of the GHA (r = 0.9) and less
representative toward the boundaries (r = 0.6–0.7). Simi-
lar to what has already been established by several studies
focusing on the Sahel (e.g., Nicholson 2009a), precipitation
varies fairly ubiquitously across the GHA as a whole (i.e.,
wet seasons are wet everywhere and dry seasons are dry
everywhere). Superimposed upon this regional mode of
variability is a dipole-like pattern that causes the north and
south to experience anomalies of opposite sign (hence the
lower correlations near the boundaries). We explore sub-
regional variability later in the drought-diagnostics portion
of this paper.
It is crucial to this study that the CHG-CLIM data
accurately reflect the direction of precipitation trends in the
GHA following the Sahel precipitation decline that ended
in the 1980s. Gaps in publicly available data and the cost of
privately held data from eastern Africa, especially from
Ethiopia, have caused previous studies to exclude parts of
the GHA (e.g., Nicholson et al. 2003; Nicholson 2005). A
recent study that employed data (137 gauge stations) pur-
chased from the Ethiopian Meteorological Agency found
good agreement with global gridded precipitation estimates
for boreal summer months. The authors concluded that the
purchased dataset is more accurate for Ethiopia than the
global products, although they did not compare multi-
decade trends among the various products (Dinku et al.
2008). The Ethiopian station data used in our study were
also purchased from the Ethiopian Meteorological Agency,
and are similar in quality and quantity to those examined
by Dinku et al. (2008).
Given a shortage of literature on JJAS precipitation
trends in the GHA following the Sahel drought, we com-
pared our calculations of post-drought trends in the GHA to
those of 10 alternate global precipitation products
(Table 1). We used CHG-CLIM and each of the 10 alternate
products to compare the direction and rate of change in
JJAS precipitation in the GHA during the 1970–1989 period
to that of the 1990–2009 period. For a wider regional per-
spective, we repeated this procedure for the Sahel region to
the west (boundary outlined in yellow in Fig. 2b). We also
evaluated precipitation trends in the GHA and Sahel from
1998 to 2009 using the satellite-derived Tropical Rainfall
Measurement Mission (TRMM) 3B43 merged product
(Kummerow et al. 2000). While TRMM 3B43 covers a
shorter time period than the other data sets used here, it is
useful for evaluating precipitation trends over the last
decade because the amount of missing gauge data over the
GHA increases during the same period (Nicholson 2005).
2.3 Precipitation and drought diagnostics
We used rotated principal components analysis (Richman
1986) to split the GHA into two (northern and southern)
sub-regions. The sub-regional precipitation records
accounted for 75% of variability in the north and 68% in
the south.
In order to evaluate Indian Monsoon-GHA precipitation
relationships (e.g., Camberlin 1997) we compared JJAS
Bombay SLP to the north and south sub-regional CHG-
CLIM precipitation time series using monthly station SLP
data from the Global Historical Climate Network (GHCN,
(ftp.ncdc.noaa.gov/pub/data/ghcn/v2/). If Bombay SLP did
not adequately represent a regional precipitation record, we
considered monthly SLP data from 1,107 alternate stations
in Africa, southern Eurasia, and the Maritime Continent (see
the ‘‘Appendix’’ for a description of SLP data processing).
We calculated the correlation between precipitation and
SLP at every pair of stations (including Bombay) from 1953
to 1988. We identified the pair of regions (clusters of
stations) where SLP records could either be summed or
subtracted to most accurately estimate JJAS precipitation.
We used three different reanalysis data sets to analyze
how sub-regional precipitation variability is related to
large-scale atmospheric circulation and vapor transports.
Fig. 2 a Time series of standardized (z-scores) seasonal JJAS
precipitation totals within the GHA from 1948 to 2009. Red line is
the mean regional record. Grey area bounds inner quartile values.
Heavy horizontal lines represent means during 1970–1989 and
1990–2009. b Mean standardized JJAS precipitation during 1990–
2009 minus mean standardized precipitation during 1970–1989.
c Rate of precipitation change between 1970–1989 and 1990–2009 at
all gauge locations with at least 10 reported JJAS precipitation totals
during each period. Black dots represent gauge locations that do not
meet this requirement. Red polygons in b and c bound the GHA
region considered in this study. Yellow polygons in b and c bound the
Sahel region considered in this study
A. P. Williams et al.: Recent summer precipitation trends
123
For the period 1948–1988 we used the NCEP/NCAR
(Kalnay et al. 1996) and ECMWF ERA-40 (Uppala et al.
2005) reanalysis climate datasets. To test whether rela-
tionships established with SLP during 1948–1988 were
stationary after the peak of the Sahel drought in 1988, we
analyzed trends in both the original NCEP/NCAR reana-
lysis and NCEP-DOE Reanalysis 2 data (NCEP2, available
for 1979–2009, Kanamitsu et al. 2002). NCEP2 uses an
updated forecast model and data-assimilation system from
the NCEP/NCAR reanalysis and it includes satellite data.
We visualized trends in, and correlations with, wind-vector
data using quiver-plot maps. In these maps, the x-axis of an
arrow refers to the zonal (u) wind component and the y-axis
refers to the meridional (v) wind component. In vertical
profile maps depicting trends in, or correlations with, both
horizontal and vertical wind velocity along a horizontal
transect, the x-axis refers to the horizontal velocity com-
ponent parallel to the transect and the y-axis refers to the
vertical velocity component.
Informed by previous research (e.g., Gill 1980, 1982;
Funk et al. 2008; Hagos and Cook 2008; Williams and
Funk 2011), we investigated whether trends in Indian
Ocean SSTs have impacted atmospheric circulation and
moisture transports in ways that would influence GHA
precipitation. We focused on the STIO region, defined here
as 15�S–0�S, 55�E–90�E. We evaluated correlations
between average JJAS SST in the STIO (NOAA extended
SST reanalysis) and spatial fields of four variables: total
cloud cover (NCEP2), net upward surface energy flux
(NCEP2), dry static energy (DSE, NCEP2), and precipi-
tation (GPCP v2.1). We calculated energy fluxes using
definitions provided by Trenberth and Stepaniak (2003a).
Net surface energy flux comprises the net upward radiation
flux from the ocean’s surface plus the upward fluxes of
sensible and latent heat from the ocean’s surface. DSE is
the sum of the energy contained in the temperature and
geopotential height of an air column.
We evaluated how higher SSTs in the STIO during recent
decades have impacted interannual relationships between
SLP and JJAS precipitation in the GHA. For each of the two
GHA sub-regions, we classified JJAS seasons from 1948 to
2009 based upon whether SLP conditions were favorable for
precipitation (‘‘wet SLP’’ and ‘‘dry SLP’’), and whether SSTs
in the STIO were warm or cool. We designated all seasons
with neutral SLP or SST conditions to a fifth ‘‘neutral’’ class.
We evaluated mean JJAS precipitation (CHG-CLIM and
Global Precipitation Climatology Centre (GPCC)) in the
GHA during each of the five classes of seasons.
2.4 Moisture source information in tree-ring oxygen-18
At the compound of the Debrebirkan Selassie church in
Gondar, Northwest Ethiopia (12�370N, 37�290E), 5 mm
diameter cores were obtained from Juniperus procera trees
in May 2007 as part of a larger sampling campaign (Wils
et al. 2011). Successful cross-dating between 32 trees from
five sites in the North Gondar zone was achieved by
comparison of the wood anatomy directly on the surface of
the samples and skeleton plotting (Stokes and Smiley
1968). Cross-dating was evaluated using the computer
program COFECHA (Holmes 1983; Grissino-Mayer 2001)
and the annual nature of the tree rings was confirmed by
AMS radiocarbon dating (Wils et al. 2010, 2011).
Oxygen isotope ratios were measured on one tree core in
a pilot study to obtain a preliminary insight into the
potential environmental sensitivity of this isotopic variable
in tree rings. Slivers were cut from absolutely dated annual
growth rings representing years 1905–2003. Lignin was
oxidized by the in situ generation of chlorine dioxide
(ClO2) and hemicelluloses were hydrolyzed from the
resulting holocellulose to yield a-cellulose (Loader et al.
1997; Rinne et al. 2005). After sample homogenization,
0.30–0.35 mg of dry a-cellulose was weighted into silver
vessels and pyrolysed over glassy carbon at 1,090�C.
Oxygen isotope ratios were measured using a PDZ Europe
20-20 mass spectrometer interfaced to a Europa ANCA
GSL elemental analyzer at Swansea University (Loader
et al. 2008). Oxygen isotope ratios are expressed as per
mille (%) deviations relative to the VSMOW standard
(Coplen 1995). Analytical precision was typically 0.3% as
was illustrated by a repeat analysis of samples from 1910
(mean = 34.67%, standard deviation = 0.3%, n = 15).
Two influential variables on tree-ring d18O are the d18O
of precipitation and the amount of isotopic enrichment of
leaf water that occurs during transpiration (Roden and
Ehleringer 1999a; Robertson et al. 2001; McCarroll and
Loader 2004). Leaf-water enrichment is driven by the water
vapor pressure deficit (VPD) of the atmosphere under a
wide range of environmental conditions (Roden and Ehle-
ringer 1999b). To test the impact of evaporative enrichment
from leaves, we statistically regressed the tree-ring d18O
record against monthly and multi-month VPD, as estimated
by NCEP2 reanalysis. A positive relationship between tree-
ring d18O and VPD would suggest evaporative enrichment
is an important driver of interannual tree-ring d18O vari-
ability. A non-positive relationship would suggest tree-ring
d18O variability is dominated by variations in d18O of
precipitation.
We compared monthly NCEP2 reanalysis data to tree-
ring d18O to test relationships between tree-ring d18O and
variables suspected of influencing the source of precipitation
in the GHA. Variables chosen were anti-cyclonic circulation
of atmospheric moisture over the Gulf of Guinea and STIO.
A good inverse proxy for anti-cyclonic moisture transports is
cloud cover. For each month and range of months, we cal-
culated the mean cloud frequency over the southern Gulf of
A. P. Williams et al.: Recent summer precipitation trends
123
Guinea (5�S–25�S, 15�W–20�E) and western STIO (2.5�S–
10�S, 50�E–67.5�E). We then found the range of months
when this cloud-cover correlated most strongly (and nega-
tively) with tree-ring d18O. Exploratory analysis revealed
that another good anti-cyclonic moisture transport index is
the sum of meridional moisture transports over southern
Africa to the east of the Gulf of Guinea (0�S–20�S, 15�E–
30�E) and zonal moisture transports in the western STIO
(2.5�S–10�S, 50�E–67.5�E). We repeated the analysis
described above for this alternate index. For visualization of
the more general circulation features associated with vari-
ability of tree-ring d18O, we also compared tree-ring d18O
to the circulation fields evaluated throughout this study.
3 Results and discussion
3.1 JJAS precipitation during 1948–2009
JJAS precipitation declined substantially throughout much
of the GHA during the 1948–2009 period. Figure 2a is a
time series of standardized JJAS precipitation totals in the
GHA region considered in this study, outlined in red in
Fig. 2b, c. Declines during the 1960s–1980s occurred in
concert with the well-known reductions throughout the
Sahel (the Sahel region considered in this study is outlined
in yellow in Fig. 2b, c). GHA precipitation decreased by
approximately 1 standard deviation during the 1950–1989
period (Fig. 2a), corresponding to decreases of over 30 mm
per decade throughout much of the Ethiopian Highlands,
over 60 mm per decade in western Sudan, and little change
in southern Sudan, northern Uganda, and western Kenya.
Following the 1980s, JJAS precipitation continued to
decline in the GHA while there was no decline in precip-
itation throughout much of the Sahel region west of Sudan
(Fig. 2). During 1990–2009, mean GHA precipitation was
reduced by more than 0.5 standard deviation units
compared to 1970–1989. Exceptions were observed in
some areas in northeastern Ethiopia and northern Sudan,
where post-1989 precipitation increased by over 0.5 stan-
dard deviation units. Notably, Fig. 14 in ‘‘Appendix’’
shows a similar spatial structure in recent precipitation
trends for the MAMJ season. The similarity in spatial
patterns of precipitation trends during JJAS and MAMJ
suggests the same mechanism may be suppressing precip-
itation during both seasons despite the fact that the circu-
lation patterns that cause precipitation during these two
season are quite different.
Since station coverage has decreased over the past
20 years (Fig. 2c), we considered 10 alternate gridded
estimates of JJAS precipitation in addition to our CHG-
CLIM data. For the GHA and Sahel regions, Table 1 lists
each additional dataset’s rate of change in mean JJAS
precipitation total comparing the 1970–1989 to the
1990–2009 period. Eight of the 10 alternate datasets agree
with CHG-CLIM that JJAS precipitation did not increase in
the GHA after the 1970–1989 period. For the Sahel region,
7 of 10 alternate datasets agree that 1990–2009 totals
exceeded 1970–1989 totals. In addition, 9 out of 10 alter-
nate datasets agree with CHG-CLIM that precipitation in
the GHA either declined more or recovered less than in the
Sahel. Notably, not all precipitation datasets cover the
entire 1970–2009 period. If the negative post-1970 pre-
cipitation trend indicated by most datasets is real, datasets
beginning in 1979 may indicate artificially low precipita-
tion totals for 1970–1989 and datasets ending before 2009
may indicate artificially high totals for 1990–2009. Despite
this bias, 5 of 6 datasets not covering the entire 1970–2009
still agree on reductions in GHA precipitation.
Finally, while the TRMM 3B43 data have limited
temporal coverage compared to the other data sets, they
indicate a substantial decline (*40%) in precipitation over
the GHA and no change in the Sahel from 1998 to 2009
(Fig. 3). Although missing data surely hinder the accuracy
Table 1 Rate of precipitation
change in the GHA and Sahel
regions from the 1970–1989
period to the 1990–2009 period
* Significant (P \ 0.05 from
two-tailed t test) changes
between time periods
Product name Data years D GHA
precipitation
(mm dec-1)
D Sahel
precipitation
(mm dec-1)
Reference
CHG-CLIM 1948–2009 -15.7 12.7 Funk et al. (2011b)
CMAP 1979–2009 -5.3 17.0 Xie and Arkin (1997)
CPC 1948–2009 -6.1 8.6 Chen et al. (2002)
CRU 1948–2009 1.9 21.3 Mitchell and Jones (2005)
ECMWF 1958–2001 -41.0* -11.9* Uppala et al. (2005)
GPCC 1948–2009 -4.1 9.4 Rudolf and Schneider (2005)
GPCP 1979–2009 4.5 41.7* Adler et al. (2003)
MergPr 1948–2008 -1.9 -3 Smith et al. (2010)
NCEP/NCAR 1948–2009 -61.9* -11.2 Kalnay et al. (1996)
NCEP2 1979–2009 -204.9* 1.3 Kanamitsu et al. (2002)
UDEL 1948–2008 -0.9 13.2 Willmott and Matsuura (1995)
A. P. Williams et al.: Recent summer precipitation trends
123
of interannual GHA precipitation estimates for all gauge-
based datasets, particularly for the southern Sudan region
(Fig. 2c), the broad consensus from the data considered
here is that recovery from the drought in the 1970s and
1980s has been at least partially suppressed in the GHA
relative to the Sahel.
3.2 Drivers of GHA precipitation and drought
during 1948–1988
NCEP/NCAR and ECMWF reanalysis datasets suggest that
JJAS vapor transports into the GHA primarily originate
from the Atlantic Ocean (Fig. 4, see Fig. 15 in ‘‘Appen-
dix’’ for the ECMWF figure). Transports into the GHA are
almost entirely confined to the lower troposphere below
700 hPa and are likely recycled through the rainforests of
the Congo Basin on their way from the Atlantic (Levin
et al. 2009). Moisture in the Congo Basin appears to mainly
originate from the Gulf of Guinea, driven by strong sub-
tropical surface highs over the southern Atlantic and Indian
Oceans and a strong low over northern Africa. Moisture is
transported from the Congo Basin toward the GHA by low-
level westerly and southwesterly winds, drawn into the
region by thermally driven low surface pressures within the
area of the Inter-Tropical Convergence Zone to the north
and east into the GHA. There is substantial interannual
variability in the amount of moisture entering the GHA via
this pathway.
South of the center of the North African surface low,
deep convection and enhanced moisture transports from the
south and west drive monsoonal precipitation in the Sahel
and GHA (Fig. 4c, d). For the Sahel, the primary driver of
interannual and longer-term variability of JJAS precipita-
tion is the north-south displacement of upper-level jet fea-
tures, zone of maximum convection, and southern boundary
of the thermal low over northern Africa (Nicholson 2009b).
Wetter years occur when these features are displaced to the
north. This rule was apparent in the northern GHA. From
1953 to 1988, northern GHA precipitation correlated very
well (r = 0.88, P \ 0.0001) with the SLP gradient between
Sudan and the southeastern coast of the Mediterranean
Sea (Fig. 5b). The rainiest seasons occurred when SLP was
anomalously high in Sudan and anomalously low to the north in Egypt and surrounding countries (see Fig. 5a for
SLP station locations and an outline of northern GHA).
Variability in this SLP gradient explains interannual swings
in JJAS precipitation as well as the Sahel-like decline in
JJAS precipitation from the mid-1950s through mid-
1980s over the GHA. For the southern GHA, JJAS precipi-
tation correlated much more strongly with Bombay SLP
(r = -0.83, P \ 0.0001) during 1953–1988 (Fig. 5c). This
corroborates the findings of Camberlin (1997), who, along
with Hoskins and Rodwell (1995), hypothesized that dia-
batic heating associated with the Indian monsoon
Fig. 3 Satellite-derived TRMM 3B43 mean regional JJAS precipi-
tation totals within the GHA (red) and Sahel (black) from 1998 to
2009
Fig. 4 Mean JJAS atmospheric circulation and moisture transports
from 1958 to 2001 according to the NCEP/NCAR reanalysis. a Lower
troposphere below 700 hPa: arrows indicate direction and velocity of
moisture transports, background represents total precipitable water.
b Mid-troposphere between 700 and 500 hPa: arrows indicate
direction and velocity of wind, background represents total precip-
itable water. c and d Vertical profiles of horizontal and vertical wind
velocity (arrows) and specific humidity (background) along the
transects defined by the c pink and d yellow dotted lines in a and
b. Grey areas at the bottom of (c) and (d) represent land. Red outlinesin a and b and red areas in c and d represent the GHA
A. P. Williams et al.: Recent summer precipitation trends
123
supported an intensified tropical easterly jet (TEJ) across
Africa and a low-level response across Africa that involved
ridging across the Sahara at *25�N, surface lows around
12�N, and westerly wind toward the GHA.
NCEP/NCAR and ECMWF reanalyses support the idea
that enhanced westerly transports across tropical Africa
contribute to enhanced GHA precipitation (e.g., Camberlin
1997; Jury 2010). Figures 6 and 7 show correlation
between various aspects of NCEP/NCAR atmospheric
circulation (including moisture transports) and JJAS pre-
cipitation in the northern and southern GHA, respectively
(see Figs. 15 and 16 in ‘‘Appendix’’ for duplications of
these figures calculated with ECMWF). Northern GHA
precipitation is correlated with a strong North African
surface low and cyclonic southwesterly winds toward
the GHA (Fig. 6a). Correlation with southwesterly wind
velocity is not confined to the lower troposphere where the
vast majority of water vapor is transported, but instead
extends throughout all altitudes below the TEJ (Fig. 6b, d).
Northern GHA precipitation is positively correlated with
an enhanced TEJ (Fig. 6c, d). Both of these results are
consistent with observations of a relatively weak African
Easterly Jet and strong TEJ during wet monsoon seasons
in the Sahel region (Kanamitsu and Krishnamurti 1978;
Newell and Kidson 1984).
While correlations between reanalysis atmospheric cir-
culation (including moisture transports) and JJAS precipi-
tation in the southern GHA were weaker than for the
northern GHA (Fig. 7), precipitation records for both
regions correlate with moisture transport traveling from the
Gulf of Guinea and across the Congo Basin (Fig. 7a, b, d).
A main difference between drivers of north versus south
GHA precipitation is that southwesterly cyclonic circula-
tion is not necessary to deliver moist air to the southern
GHA. Instead, southern GHA precipitation correlates with
purely westerly flow across Africa between the surface and
500 hPa. This westerly flow is associated with enhanced
surface low-pressure over western India and the western
Indian Ocean north of Madagascar (Fig. 7a, b), consistent
with the results of the SLP analysis. Southern GHA pre-
cipitation also correlated with ascending motion over
western Africa and on the eastern boundary of the STIO
(Fig. 7c, d). Correlations with ascending motion were
particularly strong over the STIO and weaker over the
GHA in the ECMWF analysis. In both reanalyses, corre-
lation with ascending motion was generally confined to the
mid- and upper-troposphere between 600 and 200 hPa. The
correlation fields indicate that enhanced convection in
rainy seasons corresponds with an enhanced TEJ, as was
the case for northern GHA precipitation. Previous work has
shown that an enhanced TEJ contributes to enhanced
instability throughout the atmospheric column during the
north African summer monsoon (Nicholson 2009b; Segele
et al. 2009a, b).
3.3 Post-1988 climate trends and drought diagnostics
From 1989 to 2009, the SLP gradient between Sudan and
the southern Mediterranean coast strengthened and Bom-
bay SLP declined (Fig. 5b, c). According to the strong
statistical relationships between SLP and GHA precipita-
tion, these trends should be associated with increasing
JJAS precipitation throughout the GHA region (north and
south). The fact that GHA precipitation did not rebound
following the 1980s suggests that a secondary factor
worked to suppress it and alter the previously established
relationships with SLP.
To diagnose these altered relationships, we evaluate
trends in atmospheric circulation and moisture transports in
recent decades. Figure 8 shows linear trends in atmospheric
Fig. 5 a Red and blue polygons outline the northern and southern
GHA sub-regions, respectively. Precipitation in the north was best
described by the SLP gradient between high SLP at stations in Sudan
(orange dots) and low SLP at stations in the southern Mediterranean
area (pink dots). Precipitation in the southern region was best
described by low SLP in Bombay (blue triangle). Up- and down-
pointing triangles represent positive and negative relationships,
respectively, between sub-regional precipitation and regional SLP.
b Time series of standardized JJAS precipitation in the northern GHA
(red) and estimated precipitation (black) using the linear relationship
between the Sudan-to-Mediterranean SLP gradient and JJAS precip-
itation from 1948 to 1988. c Time series of standardized JJAS
precipitation in the southern GHA (blue) and estimated precipitation
(black) using the linear relationship between Bombay SLP and JJAS
precipitation from 1948 to 1988. The vertical grey lines in b and
c divide the pre-1988 and post-1988 portions of the time series
A. P. Williams et al.: Recent summer precipitation trends
123
circulation and moisture transports from 1979 to 2009,
according to the NCEP2 reanalysis. Alternatively, see
Fig. 18 in ‘‘Appendix’’ for differences between post- and
pre-1988 periods rather than trends (calculated from the
NCEP/NCAR reanalysis). Striking features in both figures
are strong negative trend in convection and atmospheric
water vapor content within and around the GHA. These
negative trends appear to be linked to changes in SSTs and
convection over the STIO (see Fig. 8c) that have been
previously associated with negative precipitation trends
Fig. 6 Correlation between JJAS precipitation in the northern GHA
and atmospheric circulation (including moisture transports) from
1948 to 1988 (NCEP/NCAR reanalysis). Only correlations exceeding
the 90% significance level are shown. a Correlation with horizontal
moisture transports (arrows) and specific humidity (background)
within the lower troposphere below 700 hPa. b Correlation with
horizontal wind velocity (arrows) and specific humidity (background)
within the mid-troposphere between 700 and 500 hPa. c and d Vertical
profiles of correlation with horizontal and vertical wind velocity
(arrows) and specific humidity (background) along the transects
defined by the pink (c) and yellow (d) dotted lines in a and b. Arrowsin all plots represent correlation with velocity in zonal and meridional
directions (a and b) or horizontal and vertical directions (c and d).
Each arrow is the hypotenuse of an invisible triangle, of which the
lengths of the x- and y-axes indicate the strength of correlation. Greyareas at the bottom of (c) and (d) represent land. Bold red outlines in
a and b and red areas in c and d bound the northern GHA. Thin redoutlines in a and b bound the rest of the GHA
Fig. 7 Correlation between JJAS precipitation in the southern GHA
and atmospheric circulation (including moisture transports) from
1948 to 1988 (NCEP/NCAR reanalysis). Only correlations exceeding
the 90% significance level are shown. a Correlation with horizontal
moisture transports (arrows) and specific humidity (background)
within the lower troposphere below 700 hPa. b Correlation with
horizontal wind velocity (arrows) and specific humidity (background)
within the mid-troposphere between 700 and 500 hPa. c and d Vertical
profiles of correlation with horizontal and vertical wind velocity
(arrows) and specific humidity (background) along the transects
defined by the pink (c) and yellow (d) dotted lines in a and b. Arrowsin all plots represent correlation with velocity in zonal and meridional
directions (a and b) or horizontal and vertical directions (c and d).
Each arrow is the hypotenuse of an invisible triangle, of which the
lengths of the x- and y-axes indicate the strength of correlation. Greyareas at the bottom of (c) and (d) represent land. Bold red outlines in
a and b and red areas in c and d bound the southern GHA. Thin redoutlines in a and b bound the rest of the GHA
A. P. Williams et al.: Recent summer precipitation trends
123
over the GHA during boreal spring (Funk et al. 2008;
Williams and Funk 2011). In an analysis of the MAMJ
season, Williams and Funk (2011) found that a westward
extension of the Indian-Pacific tropical warm pool has led
to a westward extension of the convective branch of the
tropical Walker circulation over the STIO, and conse-
quently, a westward migration of the western descending
branch of the Walker system into northern Africa.
Dynamically, increased SST in the STIO has driven large
increases in convection and precipitation over the STIO,
and the increase in the amount of diabatic energy released
during precipitation has led to increased divergence of DSE
in the overlying mid- and upper-troposphere. This intensi-
fied outflow of mid- and upper-tropospheric DSE from the
STIO has caused increased subsidence over northern Africa
and decreased moisture transports into the GHA for at least
the past 30 years. Vertical velocity trends suggest similar
mechanisms appear to be operating during the JJAS season
(Fig. 8c, d).
The southeast-to-northwest overturning circulation trend
displayed in Fig. 8c can be characterized as a single time
series: the first principal component of all standardized
time series of specific humidity, horizontal velocity, and
vertical velocity represented in Fig. 8c in the geographic
region between 30�E–80�E and the vertical region between
the surface and 500 hPa. This principal component time
series is plotted as the black line in Fig. 9a. In the same
figure, the red line represents the seasonal JJAS time series
of mean SST in the STIO region, calculated with the
NOAA extended SST dataset. The overturning trend has
been well correlated with STIO SST since at least 1979
(r = 0.67, P \ 0.0001).
Also associated with the increase in SST over the STIO
are increases in upward energy flux (Fig. 9b), cloud cover
(Fig. 9c), and precipitation (Fig. 9d) within the STIO
region. Since 1979, NCEP2 upward energy flux from the
surface increased by approximately 43 W m-1 per �C in
the STIO, NCEP2 cloud frequency increased by 5.9% per
�C, and GPCP precipitation increased by 135 mm per �C.
In the northwestern sub-section of the STIO (0�S–10�S,
55�E–75�E), GPCP precipitation increased by 295 mm per
�C. Notably, there is considerable uncertainty in each of the
calculations presented here because they pertain to derived,
not measured, variables. There is, however, general cor-
roboration from alternate datasets that the magnitudes of
the above variables have all increased as STIO SSTs have
increased (Table 2).
Increases in SST, upward energy flux, cloud cover, and
precipitation over the STIO have been accompanied by
diabatic heating in the mid-troposphere due to the con-
version of latent energy into DSE during condensation
(Trenberth and Stepaniak 2003b). Figure 9e shows that
STIO surface warming corresponds with a significant
increase in the amount of NCEP2 DSE exported from the
region (56 Wm-2 per degree warming in the STIO). As
was the case for GPCP precipitation, divergence of DSE
increased most rapidly in the northwestern portion of the
STIO. In this region, DSE correlated well (r [ 0.77,
P \ 0.0001) with GPCP precipitation. Agreement between
these two independently derived data sets, and their cor-
relation with STIO SSTs, testifies to the strength of the
inter-connection between STIO SSTs, precipitation in the
STIO region, and the large-scale energetic response in
the atmosphere.
Fig. 8 Linear trends in JJAS atmospheric circulation and moisture
transports from 1979 to 2009 (NCEP2 reanalysis). Only trends
exceeding the 90% significance level are shown. a Trends in
horizontal moisture transports (arrows) and specific humidity (back-ground) within the lower troposphere below 700 hPa. b Trends in
horizontal wind velocity (arrows) and specific humidity (background)
within the mid-troposphere between 700 and 500 hPa. c and d Vertical
profiles of trends in horizontal and vertical wind velocity (arrows)
and specific humidity (background) along the transects defined by the
pink (c) and yellow (d) dotted lines in a and b. Grey areas at the
bottom of (c) and (d) represent land. Bold red outlines in a and b and
red areas in c and d represent the GHA
A. P. Williams et al.: Recent summer precipitation trends
123
Increasing exports of DSE from the STIO region appear
to be associated with increasing imports of DSE into the
GHA (Fig. 9e). While Fig. 9e does not explicitly link
divergence of DSE over the STIO to convergence of DSE
over the GHA, the overturning trend shown in Fig. 8c does.
This atmospheric response is in agreement with previous
climate modeling work showing that an off-equatorial
positive heating anomaly should lead to decreased con-
vective activity over eastern Africa via a Gill-type response
(e.g., Gill 1980, 1982; Funk et al. 2008). This is further
supported by Fig. 8c, d, which shows that upward vertical
velocities over the Ethiopian Highlands have slowed by as
much as 0.06 Pa s-1 (a figure close to the climatological
mean indicated in Fig. 4c, d). Finally, reduced convection
over the GHA also reduces westerly moisture transport
from the Congo Basin (Fig. 8b, d). This is particularly
apparent in maps comparing mean states of atmospheric
circulation during the 1948–1988 and 1989–2009 periods
in Fig. 18b, d in ‘‘Appendix’’ .
3.4 Classification
To more carefully evaluate how STIO warming has influ-
enced the spatial structure of JJAS precipitation in the GHA,
we utilized the higher-resolution CHG-CLIM and GPCC
interpolated precipitation products (Fig. 10 for north GHA,
Fig. 11 for south GHA). In these figures, JJAS seasons from
1948 to 2009 are split into 5 unique classes based upon both
the SLP record shown in Fig. 5 (Sudan-Mediterranean
SLP gradient for north, Bombay SLP for south) and mean
JJAS SST in the STIO. The 5 classes are (1) neutral, (2) wet
SLP/cool STIO, (3) wet SLP/warm STIO (4) dry SLP/cool
STIO, and (5) dry SLP/warm STIO. Neutral years were
years when SLP and/or SST were within 1/8 of a standard
deviation of the mean. Significantly more precipitation falls
in ‘‘wet SLP’’ seasons than in ‘‘dry SLP’’ seasons in the
north and south regions (P \ 0.0001 and P = 0.0012,
respectively). Among seasons classified as ‘‘wet SLP’’, the
‘‘warm STIO’’ seasons were significantly drier than the
‘‘cool STIO’’ seasons in the north and south (P = 0.0478
and 0.0094, respectively). Among seasons classified as ‘‘dry
SLP’’, ‘‘warm STIO’’ seasons were marginally significantly
drier than the ‘‘cool STIO’’ seasons in the north and south
(P = 0.0623 and 0.0729, respectively).
Fig. 9 a Standardized time series representing the overturning trend
(black) shown in Fig. 8c east of 30�E and mean JJAS STIO SST
(red). b–d Maps of linear relationships between JJAS STIO SST (redline in a) and b NCEP2 upward surface energy flux, c NCEP2 total
cloud cover, d GPCP precipitation, and e NCEP2 divergence of DSE.
Black boxes over the Indian Ocean bound the STIO region, redoutlines bound the GHA
Table 2 Regression of various calculations of JJAS energy flux, total
cloud cover, and precipitation in the STIO (15�S–0�S, 55�E–90�E)
versus NOAA JJAS SST in the STIO
Product name D STIO
energy flux
(W m-2 �C-1)
D STIO
cloud cover
(% �C-1)
D STIO
precipitation
(mm �C-1)
CMAP – – 2
CPC – – 15
ECMWF 23 4.5 78*
GPCP – – 135*
MergPr – – 104*
NCEP/NCAR 32* 4.5* 219*
NCEP2 43* 5.9* 529*
* Significant (P \ 0.05) relationships
A. P. Williams et al.: Recent summer precipitation trends
123
For each of the 5 classes, maps of mean CHG-CLIM
(top) and GPCC (bottom) precipitation anomaly appear
below the box plots in Figs. 10 and 11. It is clear from
these maps that the regional tendencies described above
generally hold throughout the GHA. The impact of
increasing SSTs in the STIO appears to be particularly
strong in the western Ethiopian Highlands, northern
Uganda, and the Nairobi region of Kenya. In these regions,
precipitation totals during ‘‘wet SLP/warm STIO’’ seasons
are similar to, or even lower than, totals during ‘‘dry SLP/
cool STIO’’ seasons (Fig. 11). This suggests that on a
decadal scale, circulation dynamics associated with
increasing SSTs in the STIO are becoming comparably
important in dictating JJAS precipitation totals in the
southern GHA as are the circulation dynamics associated
with Bombay SLP variability. The relatively high gauge
density throughout the Ethiopian Highlands, Uganda, and
Nairobi regions (Fig. 1c) provides confidence that this
finding is robust.
3.5 Tree-ring d18O and large-scale atmospheric
circulation and moisture transports
The tree-ring d18O record is displayed in Fig. 12a (black
line). Mean d18O from 1905 to 2003 was 32.8% with a
standard deviation of 1.7%. Values slowly declined by
about 1% from 1905 to 1960 and then declined at a rate of
0.9% per decade from 1961 to 2003. Notably, this trend is
not necessarily driven by climate change, as non-climate-
driven age trends have been shown to occur naturally in
tree-ring d18O records and can last for centuries (Esper
et al. 2010). The negative d18O trend in our Ethiopian
Highlands record, however, is an order of magnitude
stronger than those observed by Esper et al. (2010). Fur-
ther, recent work by Young et al. (2011) suggests that age
trends in tree-ring d18O may not be significant in trees
older than 50 years old. Nonetheless, more samples must
be collected and measured to enhance the authority of
interpretation of the strong negative d18O trend.
Fig. 10 (Top) Box plots of standardized JJAS precipitation totals in
the northern GHA during five unique classes of seasons. Neutral
seasons occurred when the Sudan-Mediterranean SLP gradient
(Fig. 5b) and/or STIO SST (Fig. 9a) were within 1/8 standard
deviations of the mean. Wet and dry SLP seasons occurred when the
SLP gradient exceeded 1/8 standard deviations from the mean in the
positive and negative direction, respectively. Warm and cool STIO
seasons occurred when STIO SST exceeded 1/8 standard deviations
from the mean in the positive and negative direction, respectively.
Maps show mean precipitation calculated with CHG-CLIM (topmaps) and GPCC (bottom maps) throughout northeastern Africa
during each of the five classes of seasons. Black contours in maps
bound areas where mean precipitation among wet or dry SLP seasons
was significantly different (at [90% confidence level) between cool
and warm STIO seasons
A. P. Williams et al.: Recent summer precipitation trends
123
Tree-ring d18O correlated positively but insignificantly
with JJAS VPD during 1979–2003. Positive relationships
were stronger when we considered VPD later in the
growing season, but none were statistically significant. The
weakness of the positive correlation is partly because VPD
increased from 1979 to 2003 while d18O decreased. When
linear trends are removed from d18O and VPD records,
November-December VPD correlates with tree-ring d18O
positively and significantly (r = 0.51, P = 0.0099). Maxi-
mum correlation with VPD probably occurs during
November and December because this is when VPD finally
grows large enough following the humid monsoon season
to draw down soil moisture and cause evaporative frac-
tionation within leaves as the tree increases its water-use
efficiency.
The relatively weak relationships between d18O and
VPD, and the opposing long-term trends, suggest tree-ring
d18O may be influenced by a secondary climate factor in
such a strong way as to offset the relationship between
increasing VPD and evaporative enrichment. We hypoth-
esized this secondary factor is the proportion of Highlands
precipitation derived from the Congo Basin. This is an
impossible variable to quantify, but an adequate proxy may
be the sum of moisture transports entering the GHA from
the Congo Basin area. Indeed, seasonal sums of vertically
integrated zonal and meridional vapor transports (NCEP2)
on the southwestern border of the GHA (2.5�N–5�N,
22.5�E–30�E) correlate positively and significantly with
tree-ring d18O (r = 0.48, P = 0.0144). This relationship is
no stronger than the relationship with VPD, however,
because the moisture transport and d18O time series share a
common trend.
The relatively weak relationship with southwesterly
moisture transports may indeed indicate that the relation-
ship is weak, or it may indicate that reanalysis moisture
transport data insufficiently represent seasonal variability
in the proportion of Highlands precipitation derived from
the Congo Basin. Further correlation analyses supported
the latter. Figure 13 shows correlations between tree-ring
d18O and various components of large-scale atmospheric
circulation during JJAS. In the STIO and southern Gulf of
Guinea regions, anti-cyclonic circulation in the low- and
Fig. 11 (Top) Box plots of standardized JJAS precipitation totals in
the southern GHA during five unique classes of seasons. Neutral
seasons occurred when Bombay SLP (Fig. 5c) and/or STIO SST
(Fig. 9a) were within 1/8 standard deviations of the mean. Wet and
dry SLP seasons occurred when Bombay SLP exceeded 1/8 standard
deviations from the mean in the negative and positive direction,
respectively. Warm and cool STIO seasons occurred when STIO SST
exceeded 1/8 standard deviations from the mean in the positive and
negative direction, respectively. Maps show mean precipitation
calculated with CHG-CLIM (top maps) and GPCC (bottom maps)
throughout northeastern Africa during each of the five classes of
seasons. Black contours in maps bound areas where mean precipi-
tation among wet or dry SLP seasons was significantly different
(at [90% confidence level) between cool and warm STIO seasons
A. P. Williams et al.: Recent summer precipitation trends
123
mid-troposphere, descending motion, and low specific
humidity all correlate significantly with tree-ring d18O. The
correlation pattern over the STIO is consistent with our
hypothesis that increased convergence and convection in
this region ultimately lead to decreased moisture transports
from the Congo Basin into the GHA. The correlation pat-
tern over the southern Gulf of Guinea supports previous
research indicating that intensification of the surface high
in this region drives intensified southwesterly moisture
transports toward the GHA (Segele et al. 2009a).
An all-encompassing representation of anti-cyclonic
anomalies in these regions is total cloud cover, which is
derived from satellite data in the NCEP2 reanalysis. The
sum of JJAS cloud frequency within the southern Gulf of
Guinea (5�S–25�S, 15�W–20�E) and western STIO (2.5�S–
10�S, 50�E–67.5�E) regions correlated negatively and
significantly with tree-ring d18O (r = -0.71, P \ 0.0001)
during 1979–2003. JJAS was also the range of months
when this cloud-cover index correlated most strongly
with d18O. Importantly, the strength of this negative rela-
tionship is not solely due to contrasting long-term trends.
When linear trends were removed from both time series,
correlation was still negative and significant (r = -0.60,
P = 0.0017).
Further, the amount of anomalous vapor flow away from
these regions represents how the anti-cyclonic anomalies
relate to moisture transports from the Congo Basin toward
Fig. 12 a Tree-ring record of annual d18O (black) from Ethiopian
Highlands and estimated d18O values (red) from the JJAS ‘‘anti-
cyclonic index’’ for the STIO and southern Gulf of Guinea (NCEP2).
The anti-cyclonic index is a standardized vapor-transport index minus
a standardized cloud-cover index. The vapor-transport index is the
sum of westerly vapor transports in the western STIO (2.5�S–10�S,
50�E–67.5�E) and southerly vapor transports east of the Gulf of
Guinea (0�S–20�S, 15�E–30�E). The cloud-cover index is the sum of
cloud frequency over the western STIO and southern Gulf of Guinea
(5�S–25�S, 15�W–20�E). b Tree-ring d18O residual values (black)
after the relationship with the anti-cyclonic index was removed.
Estimated Residual values (grey) using reanalysis VPD data (NCEP2)
at 12.5�N, 37.5�E, also adjusted to remove the relationship with the
anti-cyclonic index
Fig. 13 Correlation between the tree-ring record of annual d18O and
atmospheric circulation (including moisture transports) from 1979 to
2003 (NCEP2 reanalysis). Only correlations exceeding the 90%
significance level are shown. a Correlation with horizontal moisture
transports (arrows) and specific humidity (background) within the
lower troposphere below 700 hPa. b Correlation with horizontal wind
velocity (arrows) and specific humidity (background) within the mid-
troposphere between 700 and 500 hPa. c and d Vertical profiles of
correlation with horizontal and vertical wind velocity (arrows) and
specific humidity (background) along the transects defined by the pink(c) and yellow (d) dotted lines in a and b. Arrows in all plots represent
correlation with velocity in zonal and meridional directions (a and
b) or horizontal and vertical directions (c and d). Each arrow is the
hypotenuse of an invisible triangle, of which the lengths of the x- and
y-axes indicate the strength of correlation. Grey areas at the bottom of
(c) and (d) represent land. Red lines in a and b and red areas in c and
d bound the GHA. The green ‘‘x’’ in Northwest Ethiopia (a) and
(b) marks the location where the tree-ring d18O record was collected
A. P. Williams et al.: Recent summer precipitation trends
123
the GHA. The maps in Fig. 13a, b indicate that d18O is
positively correlated with westerly vapor transports away
from the STIO as well as southerly anomalies immediately
to the east of the Gulf of Guinea, which at the equator turn
into southwesterly anomalies toward the GHA. When
vertically integrated JJAS zonal vapor transports over the
western STIO (2.5�S–10�S, 50�E–67.5�E) are added to
vertically integrated JJAS meridional vapor transports to
the east of the Gulf of Guinea (0�S–20�S, 15�E–30�E), this
vapor transport index correlates positively and significantly
with tree-ring d18O (r = 0.72, P \ 0.0001). The correla-
tion strengthened when only the July–September period
was considered (r = 0.76, P \ 0.0001). Detrended, these
time series still correlate positively and significantly (for
JJAS, r = 0.66, P = 0.0003).
Because the indices of cloud cover and vapor transports
for the STIO and southern Gulf of Guinea correlated far
more strongly with tree-ring d18O than did VPD, we treated
a combination of these two time series as the primary pre-
dictor of d18O (red line in Fig. 12a). This time series of the
standardized vapor-transport record minus the standardized
cloud-cover record, termed the ‘‘anti-cyclonic index,’’
correlates better with d18O than does either record inde-
pendently (r = 0.80, P \ 0.0001). After correlation with
the anti-cyclonic index was accounted for, the residual d18O
record correlated positively and significantly with Novem-
ber-December VPD (r = 0.57, P = 0.0029, Fig. 12b).
This analysis is suggestive of a powerful link between
increasing temperatures, evaporation, convection, precipi-
tation, and divergence of DSE in the STIO and declining
moisture transports from the Congo Basin toward the
GHA. This serves as a call to action for the collection and
measurement of additional tree-ring d18O records from the
Ethiopian Highlands, as tree-ring d18O may hold valuable
clues about historical variability in the source of moisture
and how that variability has related to large-scale climate
forcing.
4 Conclusions
As pressure on earth’s resources increases, identifying and
understanding decadal climate variations will become
increasingly important. While work with global climate
models will certainly continue to be very important,
‘‘forensic climatology’’ that analyzes the cause of observed
droughts is another critical mode of inquiry. We show the
GHA is a prime target for such inquiry because this region
has experienced a declining trend in summertime mon-
soonal precipitation for approximately the past 60 years
(Figs. 2 and 5). JJAS precipitation was particularly low in
2008 and 2009, causing the US government to spend over
$2.3 billion on food aid in Sudan, Ethiopia, Kenya, and
Uganda.1 At present, parts of the GHA are in the midst of
their lowest annual rainfall year in at least the past
60 years,2 contributing to steep increases in food prices and
child malnutrition.3 On a longer time scale, lake-bed pollen
records link precipitation variability in tropical eastern
Africa with several major fluctuations in the amount of
forest cover since the last glacial maximum (Bonnefille and
Chalie 2000). It is therefore clear that GHA precipitation
totals are capable of undergoing extreme low-frequency
swings, likely in response to alterations in large-scale
ocean and atmospheric circulations.
To gain insight into whether ongoing precipitation
declines in the GHA can be expected to continue or
rebound in coming decades, we compared records of GHA
precipitation from 1948 to 2009 to records of large-scale
atmospheric circulation. During the classic Sahel precipi-
tation decline that ended in the late 1980s, precipitation in
the northern GHA correlated very strongly with the sea-
level pressure (SLP) gradient between Sudan and the
southern coast of the Mediterranean Sea (r = 0.86,
1948–1988). Variability in this SLP gradient during
1948–1988 corresponds with the multi-decade decline in
precipitation across the Sahel and GHA, and also corre-
sponds with interannual variations specific to the northern
GHA. In particular, the devastating drought year of 1984,
which led to the deaths of at least several hundreds of
thousands of Ethiopians and Sudanese (De Waal 1989;
Kidane 1989), is clearly indicated by a weak Sudan-Med-
iterranean SLP gradient (Fig. 5b). For the southern GHA,
1948–1988 precipitation correlated negatively (r = -0.83)
with average SLP in Bombay (Fig. 5c). These results
indicate that prior to and during the Sahel drought, the
strength of the summer monsoon in much of the GHA was
strongly associated with the same large-scale climate pro-
cesses that governed the strength of the monsoon
throughout much of the Sahel to the west.
Notably, relationships between regional precipitation and
these SLP indices have weakened in recent years. During
1990–2009, mean JJAS rainfall totals in the north and south
GHA continued to decline even though the SLP indices
discussed above trended in directions associated with more
favorable precipitation conditions (Fig. 5). Evaluations of
recent trends in atmospheric circulation and moisture
transports (Fig. 8) suggest that warming in the STIO
accounts for a substantial portion of these recent rainfall
declines. Given strong and consistent surface winds in the
1 http://www.usaid.gov/our_work/humanitarian_assistance/ffp/where
wework.html. Estimates based upon data available in May 2011.2 http://www.fews.net/docs/Publications/FEWS%20NET%20EA_
Historical%20drought%20context_061411.pdf.3 http://www.fews.net/docs/Publications/Horn_of_Africa_Drought_
2011_06.pdf.
A. P. Williams et al.: Recent summer precipitation trends
123
STIO, and the already warm SSTs in this region, surface
warming in the STIO during the past half century has led to
large increases in STIO evaporation. Increased evaporation
has dramatically increased the vertical flux of energy into
the STIO atmosphere. This change has been accompanied
by increases in STIO precipitation and the export of dry
static energy (DSE) toward eastern Africa (Fig. 9). These
DSE exports have had two negative impacts on GHA rain-
fall. First, they have increased DSE convergence over the
GHA, increasing atmospheric stability and subsidence.
Increased convergence of DSE over the GHA has had a
secondary (but not necessarily less important) impact on
westerly moisture transports from the Congo Basin. As DSE
divergence has increased over the STIO and convergence of
DSE increased over the GHA (Fig. 9e), ridging over the
GHA has reduced moisture transport toward the GHA from
the Congo Basin (Fig. 8, Fig. 18 in ‘‘Appendix’’).
Unfortunately, long observational records do not exist to
tell us whether moisture transports from the moist Congo
Basin have truly been redirected away from the GHA. Our
analysis suggests, however, that clues may exist in the
stable isotopic composition of annual growth rings in trees
from the Ethiopian Highlands. While tree-ring d18O is
often positively influenced by warm, dry conditions due to
evaporative enrichment of leaf water, our analysis indicates
that these conditions have only a secondary impact on tree-
ring d18O. We found that large-scale circulation features
that affect transport of isotopically enriched atmospheric
moisture from the Congo Basin toward the GHA are the
primary influences on tree-ring d18O (Figs. 12 and 13).
While this finding comes from only one tree and replication
from additional samples will be crucial, this tree-ring
analysis corroborates our other findings and may serve as a
novel approach toward providing an answer to the difficult
and complicated question of how large-scale climate var-
iability impacts moisture transports into the GHA.
According to the latest report by the Intergovernmental
Panel on Climate Change, observations of decreased JJAS
precipitation in the GHA are in contrast to model projec-
tions of unchanged summer precipitation (Christiansen et al.
2007). Importantly, there is high uncertainty in model pro-
jections of precipitation and high variability among models
regarding projections of precipitation in the GHA (Chris-
tiansen et al. 2007). While these models do not converge
upon a connection between JJAS precipitation in the GHA
and greenhouse-gas induced global climate change, data
presented here suggest recent (post-1980s) drying trends
can be largely attributed to a cascade of climate changes
linked to warming trends in the STIO. Climate modeling
studies implicate greenhouse gas and aerosol emissions as
the primary causes of recent STIO surface warming (Pierce
et al. 2006; Alory et al. 2007). Although warming-induced
increases in evaporation and cloud cover work to dampen
surface warming (Du and Xie 2008), climate models predict
tropical Indian Ocean SSTs will continue increasing (Meehl
et al. 2007). Resulting increases in evaporation, rainfall, and
divergence of DSE in the STIO region (Fig. 9) are linked to
diminished rainfall in the GHA following the classic Sahel
drought of the 1970s and 1980s (Fig. 2). Unfortunately,
these declines have occurred in extremely fragile and water-
insecure regions where they have likely exacerbated social
conflict (Funk et al. 2011a) and drought-induced forest
mortality (Allen et al. 2010). The fact that swings in pre-
cipitation appear to have significantly impacted forest cover
in the past (Bonnefille and Chalie 2000) suggests that GHA
forest cover is at risk of decline due to declining JJAS
precipitation. Beyond the direct influence that water short-
age has on humans in the GHA, drought-driven forest
mortality in the Ethiopian Highlands and Ugandan rain-
forest would likely have indirect effects on humans by
influencing regional climate, the global carbon balance, and
human access to important natural resources.
Acknowledgments This research was supported by the U.S.
Agency for International Development Famine Early Warning System
Network under U.S. Geological Survey Cooperative Agreement
#G09AC00001 and the National Aeronautics and Space Administra-
tion under Precipitation Science Grant #NNX07AG266. Park Wil-
liams and Sara Rauscher gratefully acknowledge support from the
U.S. Department of Energy through the LANL/LDRD Program. Iain
Robertson is supported by the Climate Change Consortium of Wales.
Marcin Koprowski was supported by an International Incoming Short
Visit from the Royal Society (UK). Ethiopian precipitation data were
provided by the Ethiopian Meteorological Agency. Thanks to Tufa
Dinku for assisting the authors in obtaining this data purchase, pro-
viding the best possible foundation for an analysis of trends in
Ethiopia. Thanks to Greg Husak for his invaluable part in obtaining
CHG funding. Comments by Amy McNally, Brian Osborn, Charles
Jones, Ellen Mosley-Thompson, Harikishan Jayanthi, Kevin An-
chukaitis, Kyle Cavanaugh, Leila Carvalho, Lonnie Thompson,
Naomi Levin, Tom Adamson, and two anonymous reviewers greatly
improved the quality of the manuscript. NCEP/NCAR data were
accessed online through the NOAA Earth System Research Labora-
tory (ESRL) Physical Sciences Division (PSD, http://www.esrl.
noaa.gov/psd). ECMWF data were produced by the European Centre
for Medium-Range Weather Forecasts ERA-40 project (http://www.
data.ecmwf.int/data/). GHCN data were accessed from the National
Climate Data Center (ftp://ncdc.noaa.gov/pub/data/ghcn/v2/).
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
Appendix
CHG-CLIM precipitation dataset
CHG-CLIM is produced in two steps: first monthly long-
term mean fields are created, representing the spatial
A. P. Williams et al.: Recent summer precipitation trends
123
variation of the data; then seasonal station anomalies are
interpolated representing the temporal variations. JJAS
rainfall climatologies are produced using moving window
regressions, long-term (1960–1989) mean in situ observa-
tions, and background images of predictors (Funk et al.
2011b). The main predictors used are long-term averages
of satellite rainfall estimates and land surface temperature
retrievals. Because these fields directly relate to hydrologic
processes in the semi-tropics, they provide a great deal of
information about long-term mean rainfall. Latitude, lon-
gitude, and elevation are used as physiographic predictors.
The at-station differences between the moving window
regressions and average gauge observations are then
interpolated using kriging (a geostatistical interpolation
approach in which optimal interpolation weights are
determined by the spatial covariance structure of the
observed data), producing the final rainfall climatology
along with the associated kriging standard errors.
Seasonal (JJAS) at-station percent anomalies were then
interpolated using kriging (Journel and Huijbregts 1978).
Using anomalies helps mitigate the impacts of changes in
the reporting network. Square roots of rainfall percents
are used to minimize the effects of skewness. Interpola-
tion is carried out based on the spatial semivariogram,
which depicts the relationship between inter-station dis-
tances and the inter-station covariances. This semivario-
gram is estimated based on the average of the 30
1960–1989 variograms. Interpolation via kriging produces
0.25� estimates of percent rainfall, and the associated
kriging standard errors, in units of percent rainfall. The
average 1948–2009 kriging standard errors are less than
25% for regions evaluated here. These percentage
anomalies and standard errors were multiplied by the
rainfall climatology to produce rainfall and standard error
estimates in mm per season. While the year-to-year var-
iability in the region is substantial, the coherent spatial
covariance and reasonably dense gauge density combine
to produce reasonably accurate depictions of rainfall. The
CHG-CLIM dataset is discussed in greater detail in Funk
et al. (2011b).
GHCN SLP records
There are many data gaps in the monthly GHCN SLP data.
For many stations, however, data from surrounding sta-
tions can be used to fill gaps. We refer to the station
subject to gap filling as the ‘‘target station.’’ For each
target station, we considered all stations within a 400 km
radius. Considering 1 month at a time, we calculated the
linear correlation between SLP at the target station and
each surrounding station. At this point, we only considered
surrounding stations that fit at least one of the following
criteria: (1) more than 10 years of overlap and a
correlation of more than 0.85 with the target station SLP,
(2) more than 15 years of overlap and a correlation of
more than 0.75 with the target station SLP, or (3) more
than 30 years of overlap and a correlation of more than 0.7
with the target station SLP. We ranked qualifying stations
by correlation coefficient from high to low. By order of
rank, we consecutively used each surrounding station to
fill any possible gaps. We filled gaps using the linear
relationship between the target and surrounding stations.
We repeated this process three more times because each
target station, if it received new data, experienced an
altered ability to act as a gap-filling surrounding station for
other target stations.
After using surrounding stations to fill monthly gaps, we
used NCEP2 SLP data to fill monthly gaps from 1979 to
2009. For each target station, we considered each of the
nine surrounding NCEP2 grid cells (2.5� spatial resolution)
as surrounding stations. As long as there were more than
10 years of overlapping data and correlation was greater
than 0.6, we used data from the grid cell with the highest
correlation to fill gaps. We used easier criteria for gap
filling with the NCEP2 data because the short reanalysis
record does not allow for as many years of overlap with the
station data, and we were most concerned with capturing
directional trends for 1989–2009 to determine whether
recent trends in SLP are consistent with trends in
precipitation.
After using NCEP2 to fill gaps, we once again used
surrounding stations to fill more monthly gaps. Finally, we
calculated mean SLP records for JJAS at each station and
used surrounding stations to fill gaps in JJAS data. Despite
these rather extravagant gap-filling methods and relaxed
criteria for using NCEP2 data, we found strong regional
SLP signals and strong relationships between SLP and
JJAS precipitation in the GHA. These strong relationships
testify to the overall reliability of monthly GHCN and
NCEP2 SLP data.
See Figs. 14, 15, 16, 17, and 18.
Fig. 14 March–June (MAMJ) precipitation trends. Rate of MAMJ
precipitation change between 1970–1989 and 1990–2009 at all gauge
locations with at least 10 reported MAMJ precipitation totals during
each period. Black dots represent gauge locations that do not meet this
requirement. The red polygon and yellow polygons bound the GHA
and Sahel regions considered in this study
A. P. Williams et al.: Recent summer precipitation trends
123
Fig. 15 Mean JJAS atmospheric circulation and moisture transports
from 1958 to 2001 according to the ECMWF reanalysis. a Lower
troposphere below 700 hPa: arrows indicate direction and velocity of
moisture transports, background represents total precipitable water.
b Mid-troposphere between 700 and 500 hPa: arrows indicate
direction and velocity of wind, background represents total precip-
itable water. c and d Vertical profiles of horizontal and vertical wind
velocity (arrows) and specific humidity (background) along the
transects defined by the pink (c) and yellow (d) dotted lines in a and
b. Grey areas at the bottom of (c) and (d) represent land. Red outlinesin a and b and red areas in c and d represent the GHA
Fig. 16 Correlation between JJAS precipitation in the northern GHA
and atmospheric circulation (including moisture transports) from
1958 to 1988 (ECMWF reanalysis). Only correlations exceeding the
90% significance level are shown. a Correlation with horizontal
moisture transports (arrows) and specific humidity (background)
within the lower troposphere below 700 hPa. b Correlation with
horizontal wind velocity (arrows) and specific humidity (background)
within the mid-troposphere between 700 and 500 hPa. c and d Vertical
profiles of correlation with horizontal and vertical wind velocity
(arrows) and specific humidity (background) along the transects
defined by the pink (c) and yellow (d) dotted lines in a and b. Greyareas at the bottom of (c) and (d) represent land. Bold red outlines in
a and b and red areas in c and d bound the northern GHA. Thin redoutlines in a and b bound the rest of the GHA
A. P. Williams et al.: Recent summer precipitation trends
123
References
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J,
Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind
J, Arkin P (2003) The version 2 Global Precipitation Climatol-
ogy Project (GPCP) monthly precipitation analysis (1979-
present). J Hydrometeorol 4:1147–1167
Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N,
Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg E,
Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim
J-H, Allard G, Running SW, Semerci A, Cobb N (2010) A global
overview of drought and head-induced tree mortality reveals
emerging climate change risks for forests. For Ecol Manag
259:660–684
Alory G, Wijffels S, Meyers G (2007) Observed temperature trends in
the Indian Ocean over 1960–1999 and associated mechanisms.
Geophys Res Lett 34(2):L02606
Anchukaitis KJ, Evans MN (2010) Tropical cloud forest climate
variability and the demise of the Monteverde golden toad. Proc
Nat Acad Sci 107(11):5036–5040
Bhatt US (1989) Circulation regimes of rainfall anomalies in the
African-South Asian monsoon belt. J Clim 2(10):1133–1144
Fig. 17 Correlation between JJAS precipitation in the southern GHA
and atmospheric circulation (including moisture transports) from
1958 to 1988 (ECMWF reanalysis). Only correlations exceeding the
90% significance level are shown. a Correlation with horizontal
moisture transports (arrows) and specific humidity (background)
within the lower troposphere below 700 hPa. b Correlation with
horizontal wind velocity (arrows) and specific humidity (background)
within the mid-troposphere between 700 and 500 hPa. c and d Vertical
profiles of correlation with horizontal and vertical wind velocity
(arrows) and specific humidity (background) along the transects
defined by the pink (c) and yellow (d) dotted lines in a and b. Greyareas at the bottom of (c) and (d) represent land. Bold red outlines in
a and b and red areas in c and d bound the southern GHA. Thin redoutlines in a and b bound the rest of the GHA
Fig. 18 Change in mean atmospheric circulation and moisture
transports between the 1948–1988 period and the 1989–2009 period
(NCEP/NCAR reanalysis). Only changes exceeding the 90% signif-
icance level according to a 2-tailed student’s t test are shown.
a Changes in horizontal moisture transports (arrows) and specific
humidity (background) within the lower troposphere below 700 hPa.
b Changes in horizontal wind velocity (arrows) and specific humidity
(background) within the mid-troposphere between 700 and 500 hPa.
c and d Vertical profiles of changes in horizontal and vertical wind
velocity (arrows) and specific humidity (background) along the
transects defined by the pink (c) and yellow (d) dotted lines in a and
b. Grey areas at the bottom of (c) and (d) represent land. Bold redoutlines in a and b and red areas in c and d represent the GHA
A. P. Williams et al.: Recent summer precipitation trends
123
Bonnefille R, Chalie F (2000) Pollen-inferred precipitation time-
series from equatorial mountains, Africa, the last 40 kyr BP.
Global Planet Change 26(1–3):25–50
Camberlin P (1995) June-September rainfall in North-Eastern Africa
and atmospheric signals over the tropics: a zonal perspective. Int
J Climatol 15(7):773–783
Camberlin P (1997) Rainfall anomalies in the source region of the
Nile and their connection with the Indian summer monsoon.
J Clim 10(6):1380–1392
Chen M, Xie P, Janowiak JE, Arkin PA (2002) Global land
precipitation: a 50-yr monthly analysis based on gauge obser-
vations. J Hydrometeorol 3(3):249–266
Christiansen JH, Hewitson A, Busuioc A, Chen A, Gao X, Held I,
Jones R, Kolli RK, Kwon WT, Laprise R, Magana Rueda V,
Mearns L, Menendez CG, Raisanen J, Rinke A, Sarr A, Whetton
P (2007) Regional climate projections, ch. 11. In: Solomon S,
Qin D, Manning M et al (eds) Climate change 2007: the physical
science basis. Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panel on
Climate Change. Cambridge University Press, Cambridge, UK,
pp 849–940
Cook KH (2008) Climate science: the mysteries of Sahel droughts.
Nat Geosci 1(10):647–648
Coplen TB (1995) Discontinuance of SMOW and PDB. Nature
373:285
Dansgaard W (1964) Stable isotopes in precipitation. Tellus
16(4):436–468
De Waal A (1989) Famine mortality: a case study of Darfur, Sudan
1984–5. Popul Stud 42(1):5–24
Dinku T, Connor SJ, Ceccato P, Ropelewski CF (2008) Comparison
of global gridded precipitation products over a mountainous
region of Africa. Int J Climatol 28(12):1627–1638
Du Y, Xie SP (2008) Role of atmospheric adjustments in the tropical
Indian Ocean warming during the 20th century in climate
models. Geophys Res Lett 35(8):391–405
Esper J, Frank DC, Battapaglia G, Buntgen U, Holert C, Treydte K,
Siegwolf R, Saurer M (2010) Low-frequency noise in d13C and
d18O tree ring data: a case study of Pinus unicata in the Spanish
Pyrenees. Glob Biogeochem Cycle 24. doi:10.1029/2010
GB003772
Folland CK, Palmer TN, Parker DE (1986) Sahel rainfall and
worldwide sea temperatures, 1901–85. Nature 320(6063):
602–607
Funk C, Brown ME (2009) Declining global per capita agricultural
production and warming oceans threaten food security. Food Sec
1(1):271–289
Funk C, Michaelsen J (2004) A simplified diagnostic model of
orographic rainfall for enhancing satellite-based rainfall esti-
mates in data-poor regions. J Appl Meteorol 43:1366–1378
Funk C, Verdin JP (2009) Real-time decision support systems: the
famine early warning system network. In: Gebremichael M,
Hossain F (eds) Satellite rainfall applications for surface
hydrology. Springer, Netherlands, pp 295–320
Funk C, Asfaw A, Steffen P, Senay GB, Rowland J, Verdin JP (2003)
Estimating Meher crop production using rainfall in the ‘long
cycle’ region of Ethiopia. FEWS NET Rpecial Report
Funk C, Husak G, Michaelsen J, Love T, Pedreros D (2007) Third
generation rainfall climatologies: satellite rainfall and topogra-
phy provide a basis for smart interpolation. In: Proceedings of
the JRC—FAO Workshop, Nairobi, Kenya
Funk C, Dettinger MD, Michaelsen JC, Verdin JP, Brown ME,
Barlow M, Hoell A (2008) Warming of the Indian Ocean
threatens eastern and southern African food security but could be
mitigated by agricultural development. Proc Natl Acad Sci USA
105(31):11081–11086
Funk C, Eilerts G, Verdin J, Rowland J, Marshall M (2011a) A
climate trend analysis of Sudan. USGS Fact Sheet, vol 3072. US
Geological Survey, Sioux Falls
Funk C, Michaelsen J, Marshall M (2011b) Mapping recent decadal
climate variations in Eastern Africa and the Sahel. In: Wardlow
B, Anderson M, Verdin J (eds) Remote sensing of drought:
innovative monitoring approaches. Taylor and Francis, London
Gat JR, Matsui E (1991) Atmospheric water balance in the Amazon
Basin: an isotopic evapotranspiration model. J Geophys Res
96(D7):13179–13188
Gatebe CK, Tyson PD, Annegarn H, Piketh S, Helas G (1999) A
seasonal air transport climatology for Kenya. J Geophys Res
104:14237–14244
Gessler A, Brandes E, Buchmann N, Helle G, Rennenberg H, Barnard
RL (2009) Tracing carbon and oxygen isotope signals from
newly assimilated sugars in the leaves to the tree ring archive.
Plant Cell Environ 32(7):780–795
Giannini A, Saravanan R, Chang P (2003) Oceanic forcing of Sahel
rainfall on interannual to interdecadal time scales. Science
302(5647):1027–1030
Giannini A, Biasutti M, Verstraete MM (2008) A climate model-based
review of drought in the Sahel: desertification, the re-greening
and climate change. Global Planet Change 64(3–4):119–128
Gill AE (1980) Some simple solutions for heat-induced tropical
circulation. Royal Meteorol Soc Q J 106:447–462
Gill AE (1982) Atmosphere-ocean dynamics, vol 30. International
Geophysics Series, Academic Press, San Diego
Grissino-Mayer HD (2001) Evaluating crossdating accuracy: a
manual and tutorial for the computer program COFECHA.
Tree-Ring Res 57(2):205–221
Hagos SM, Cook KH (2008) Ocean warming and late-twentieth-
century Sahel drought and recovery. J Clim 21(15):3797–3814
Hastenrath S (1990) Decadal-scale changes of the circulation in the
tropical Atlantic sector associated with Sahel drought. Int J
Climatol 10(5):459–472
Hoerling M, Hurrell J, Eischeid J, Phillips A (2006) Detection and
attribution of twentieth-century northern and southern African
rainfall change. J Clim 19(16):3989–4008
Holmes RL (1983) Computer assisted quality control in tree-ring
dating and measurement. Tree-Ring Bull 43:69–78
Hoskins BJ, Rodwell MJ (1995) A model of the Asian summer
monsoon. Part I: the global scale. J Atmos Sci 52(9):1329–1340
Journel AG, Huijbregts C (1978) Mining geostatistics. Academic
press, London
Jury MR (2010) Ethiopian decadal climate variability. Theor Appl
Climatol 101(1):29–40
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L,
Iredell M, Saha S, White G, Woollen J (1996) The NCEP/NCAR
40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471
Kanamitsu M, Krishnamurti T (1978) Northern summer tropical
circulations during drought and normal rainfall months. Mon
Weather Rev 106:331
Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo JJ, Fiorino
M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull
Am Meteorol Soc 83(11):1631–1643
Kidane A (1989) Demograhic consequences of the 1984–1985
Ethiopian famine. Demography 26(3):515–522
Kucharski F, Bracco A, Yoo JH, Tompkins AM, Feudale L, Ruti P,
Dell’Aquila A (2009) A Gill–Matsuno type mechanism explains
the tropical Atlantic influence on African and Indian monsoon
rainfall. Q J Royal Meteorol Soc 135(640):569–579
Kummerow C, Simpson J, Thiele O, Barnes W, Chang ATC, Stocker
E, Adler RF, Hou A, Kakar R, Wentz F (2000) The status of the
Tropical Rainfall Measuring Mission (TRMM) after two years in
orbit. J Appl Meteorol 39(12):1965–1982
A. P. Williams et al.: Recent summer precipitation trends
123
Levin NE, Zipser EJ, Cerling TE (2009) Isotopic composition of
waters from Ethiopia and Kenya: insights into moisture sources
for eastern Africa. J Geophys Res 114(D23):D23306
Loader NJ, Robertson I, Barker AC, Switsur VR, Waterhouse JS
(1997) An improved technique for the batch processing of small
wholewood samples to a-cellulose. Chem Geol 136:313–317
Loader NJ, Santillo PM, Woodman-Ralph JP, Rolfe JE, Hall MA,
Gagen M, Robertson I, Wilson R, Froyd CA, McCarroll D
(2008) Multiple stable isotopes from oak trees in southwestern
Scotland and the potential for stable isotope dendroclimatology
in maritime climatic regions. Chem Geol 252(1–2):62–71
McCarroll D, Loader NJ (2004) Stable isotopes in tree rings. Quat Sci
Rev 23:771–801
Meehl GA, Stocker TF, Collins WD, Friedlingstein AT, Gaye AT,
Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper
SCB, Watterson IG, Weaver AJ, Zhao Z-C (2007) Global
climate projections, ch. 10. In: Solomon S, Qin D, Manning M
et al. (eds) Climate change 2007: the physical science basis.
Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge, UK, pp 749–845
Mitchell TD, Jones PD (2005) An improved method of constructing a
database of monthly climate observations and associated high
resolution grids. Int J Climatol 25(6):693–712
Newell RE, Kidson JW (1984) African mean wind changes between
Sahelian wet and dry periods. Int J Climatol 4(1):27–33
Nicholson SE (2000) The nature of rainfall variability over Africa on
time scales of decades to millenia. Global Planet Change
26(1–3):137–158
Nicholson SE (2005) On the question of the ‘‘recovery’’ of the rains in
the West African Sahel. J Arid Environ 63(3):615–641
Nicholson SE (2009a) On the factors modulating the intensity of the
tropical rainbelt over West Africa. Int J Climatol 29(5):673–689
Nicholson SE (2009b) A revised picture of the structure of the
‘‘monsoon’’ and land ITCZ over West Africa. Clim Dyn
32(7):1155–1171
Nicholson SE, Grist JP (2003) The seasonal evolution of the
atmospheric circulation over West Africa and equatorial Africa.
J Clim 16(7):1013–1030
Nicholson SE, Some B, McCollum J, Nelkin E, Klotter D, Berte Y,
Diallo BM, Gaye I, Kpabeba G, Ndiaye O (2003) Validation of
TRMM and other rainfall estimates with a high-density gauge
dataset for West Africa. Part I: validation of GPCC rainfall
product and pre-TRMM satellite and blended products. J Appl
Meteorol 42(10):1337–1354
Pierce DW, Barnett TP, AchutaRao KM, Gleckler PJ, Gregory JM,
Washington WM (2006) Anthropogenic warming of the oceans:
observations and model results. J Clim 19(10):1873–1900
Richman MB (1986) Rotation of principal components. J Climatol
6(3):293–335
Riddle EE, Cook KH (2008) Abrupt rainfall transitions over the
Greater Horn of Africa: observations and regional model
simulations. J Geophys Res 113(D15):D15109
Rinne KT, Boettger T, Loader NJ, Robertson I, Switsur VR,
Waterhouse JS (2005) On the purification of a-cellulose from
resinous wood for stable isotope (H, C, and O) analysis. Chem
Geol 222:75–82
Robertson I, Waterhouse JS, Barker AC, Carter AHC, Switsur VR
(2001) Oxygen isotope ratios of oak in east England: implica-
tions for reconstructing the isotopic composition of precipitation.
Earth Planet Sci Lett 191(1–2):21–31
Roden JS, Ehleringer JR (1999a) Hydrogen and oxygen isotope ratios
of tree-ring cellulose for riparian trees grown long-term under
hydroponically controlled environments. Oecologia 121(4):
467–477
Roden JS, Ehleringer JR (1999b) Observations of hydrogen and
oxygen isotopes in leaf water confirm the Craig-Gordon model
under wide-ranging environmental conditions. Plant Physiol
120(4):1165
Roden JS, Lin G, Ehleringer JR (2000) A mechanistic model for
interpretation of hydrogen and oxygen isotope ratios in tree-ring
cellulose. Geochim Cosmochim Acta 64(1):21–35
Rotstayn LD, Lohmann U (2002) Tropical rainfall trends and the
indirect aerosol effect. J Clim 15(15):2103–2116
Rozanski K, Araguas-Araguas L, Gonfiantini R (1996) Isotope
patterns of precipitation in the East African region. In: Johnson
TC, Odada EO (eds) The limnology, climatology, and paleocli-
matology of the East African Lakes. Gordon and Breach
Publishers, Newark, pp 79–94
Rudolf B, Schneider U (2005) Calculation of gridded precipitation
data for the global land-surface using in-situ gauge observations.
In: Proceedings of the 2nd workshop of the International
Precipitation Working Group IPWG, Monterey October 2004,
EUMETSAT, pp 231–247. ISBN 92-9110-070-6
Salati E, Dall’Olio A, Matsui E, Gat JR (1979) Recycling of water in
the Amazon basin: an isotopic study. Water Resour Res
15(5):1250–1258
Segele ZT, Lamb PJ, Leslie LM (2009a) Large-scale atmospheric
circulation and global sea surface temperature associations with
Horn of Africa June-September rainfall. Int J Climatol 29(8):
1075–1100
Segele ZT, Lamb PJ, Leslie LM (2009b) Seasonal-to-interannual
variability of Ethiopian/Horn of Africa monsoon. Part I:
associations of wavelet-filtered large-scale atmospheric circula-
tion and global sea surface temperature. J Clim 22(12):3396–
3421
Seleshi Y, Zanke U (2004) Recent changes in rainfall and rainy days
in Ethiopia. Int J Climatol 24(8):973–983
Smith TM, Arkin PA, Sapiano MRP, Chang C-Y (2010) Merged
statistical analyses of historical monthly precipitation anomalies
beginning in 1900. J Clim 23(21):5755–5770
Sonntag C, Klitzsch E, Lohnert EP, El-Shazly EM, Munnich KO,
Junghans C, Thorweihe U, Weistroffer K, Swailem FM (1979)
Palaeoclimatic information from deuterium and oxygen-18 in
carbon-14 dated north Saharian groundwaters; groundwater
formation in the past. In: Isotope hydrology. International
Atmospheric Energy Agency, Vienna, pp 569–581
Stokes MA, Smiley TL (1968) An introduction to tree-ring dating.
University of Chicago Press, Chicago
Trenberth KE, Stepaniak DP (2003a) Covariability of components of
poleward atmospheric energy transports on seasonal and inter-
annual timescales. J Clim 16(22):3691–3705
Trenberth KE, Stepaniak DP (2003b) Seamless poleward atmospheric
energy transports and implications for the Hadley circulation.
J Clim 16(22):3706–3722
Uppala SM, KAllberg PW, Simmons AJ, Andrae U, Bechtold V,
Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X,
Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K,
Balmaseda MA, Beljaars ACM, Van De Berg L, Bidlot J,
Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M,
Fisher M, Fuentes M, Hagemann S, Holm E, Hoskins BJ, Isaksen
L, Janssen PAEM, Jenne R, Mcnally AP, Mahfouf J-F, Morcrette
J-J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE,
Untch A, Vasiljevic D, Verterbo P, Wollen J (2005) The ERA-40
re-analysis. Q J Royal Meteorol Soc 131 (612):2961–3012
Verdin J, Funk C, Senay G, Choularton R (2005) Climate science and
famine early warning. Philos Trans Royal Soc B Biol Sci
360(1463):2155–2168
Whetton P, Rutherfurd I (1994) Historical ENSO teleconnections in
the Eastern Hemisphere. Clim Chang 28(3):221–253
A. P. Williams et al.: Recent summer precipitation trends
123
Williams AP, Funk C (2011) A westward extension of the warm pool
leads to a westward extension of the Walker circulation, drying
eastern Africa. Climate Dynamics. doi:10.1007/s00382-010-
0984-y
Willmott CJ, Matsuura K (1995) Smart interpolation of annually
averaged air temperature in the United States. J Appl Meteorol
34(12):2577–2586
Wils THG, Robertson I, Eshetu Z, Koprowski M, Sass-Klaassen
UGW, Touchan R, Loader NJ (2010) Towards a reconstruction
of Blue Nile baseflow from Ethiopian tree rings. The Holocene
20(6):837–848
Wils THG, Robertson I, Eshetu Z, Touchan R, Sass-Klaassen U,
Koprowski M (2011) Crossdating Juniperus procera from North
Gondar, Ethiopia. Trees Struct Funct 25:71–82
Xie P, Arkin PA (1997) Global precipitation: A 17-year monthly analysis
based on gauge observations, satellite estimates, and numerical
model outputs. Bull Am Meteorol Soc 78(11):2539–2558
Yapp CJ, Epstein S (1982) Climatic significance of the hydrogen
isotope ratios in tree cellulose. Nature 297:636–639
Young GHF, Demmler JC, Gunnarson BE, Kirchhefer AJ, Loader NJ,
McCarroll D (2011) Age trends in the tree ring growth and
isotopic archives: a case study of Pinus sylvestris L. from
northwestern Norway. Global Biogeochemical Cycles 25. doi:
10.1029/2010GB003913
Zimmermann U, Ehhalt DH, Munnich KO (1967) Soil water
movement and evapotranspiration: changes in isotopic compo-
sition of water. In: Isotopes in hydrology. International Atomic
Energy Agency, Vienna, pp 657–585
A. P. Williams et al.: Recent summer precipitation trends
123